{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "N <- 500\n",
    "I <- 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in rnorm(N, mean = 0, sd = 1): object 'N' not found\n",
     "output_type": "error",
     "traceback": [
      "Error in rnorm(N, mean = 0, sd = 1): object 'N' not found\nTraceback:\n",
      "1. rnorm(N, mean = 0, sd = 1)"
     ]
    }
   ],
   "source": [
    "tau <- rnorm(N, mean = 0, sd = 1)\n",
    "tau <- array(tau, dim = c(N, 1))\n",
    "time_intense <- array(rnorm(I, 4, 0.5), dim = c(1, I))\n",
    "time_discrimination <- array(runif(I, 1, 5), dim = c(1, I))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in print(dim(tau)): object 'tau' not found\n",
     "output_type": "error",
     "traceback": [
      "Error in print(dim(tau)): object 'tau' not found\nTraceback:\n",
      "1. print(dim(tau))"
     ]
    }
   ],
   "source": [
    "print(dim(tau))\n",
    "print(dim(time_intense))\n",
    "print(dim(time_discrimination))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "rt <- array(NA, dim = c(N,I))\n",
    "for (n in 1:N){\n",
    "    for (i in 1:I){\n",
    "        rt[n,i] <- rlnorm(1, time_discrimination[,i] - tau[n,],1/time_discrimination^2)\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 10 × 20 of type dbl</caption>\n",
       "<tbody>\n",
       "\t<tr><td>19.575269</td><td> 9.736831</td><td>11.918005</td><td> 45.99760</td><td> 3.648907</td><td> 2.5784951</td><td> 34.45140</td><td> 33.24528</td><td>18.493514</td><td> 7.586944</td><td> 34.47187</td><td> 8.502502</td><td> 4.789169</td><td> 8.425440</td><td> 25.32743</td><td> 3.844023</td><td> 35.53169</td><td> 27.05855</td><td>11.721207</td><td> 60.34530</td></tr>\n",
       "\t<tr><td>12.070171</td><td> 6.131606</td><td> 8.046342</td><td> 28.11796</td><td> 2.466564</td><td> 1.3273041</td><td> 31.93427</td><td> 19.18484</td><td>15.184394</td><td> 5.087324</td><td> 18.22168</td><td> 5.176799</td><td> 3.592614</td><td> 5.859558</td><td> 17.07175</td><td> 2.074506</td><td> 24.11629</td><td> 13.17105</td><td> 7.189315</td><td> 45.39134</td></tr>\n",
       "\t<tr><td>50.718936</td><td>25.271544</td><td>33.163816</td><td>105.56265</td><td> 8.707644</td><td> 5.6133327</td><td>111.64650</td><td> 80.88702</td><td>52.891471</td><td>20.047222</td><td> 87.08436</td><td>21.658894</td><td>13.387657</td><td>23.198969</td><td> 68.64448</td><td> 9.482063</td><td> 90.73375</td><td> 63.37555</td><td>31.235192</td><td>191.66588</td></tr>\n",
       "\t<tr><td>24.850634</td><td>12.467870</td><td>14.588572</td><td> 51.26094</td><td> 4.198930</td><td> 3.1263918</td><td> 57.58208</td><td> 42.25084</td><td>24.161054</td><td> 9.820436</td><td> 47.76637</td><td>13.124946</td><td> 6.436791</td><td>12.376809</td><td> 33.32568</td><td> 4.739224</td><td> 51.69930</td><td> 32.02140</td><td>16.057208</td><td> 92.72647</td></tr>\n",
       "\t<tr><td>26.243925</td><td>12.708966</td><td>18.181190</td><td> 57.49178</td><td> 4.373927</td><td> 2.9283865</td><td> 61.06450</td><td> 51.11109</td><td>23.624734</td><td> 9.403672</td><td> 43.08685</td><td>11.952294</td><td> 7.584674</td><td>12.718687</td><td> 33.28133</td><td> 4.426316</td><td> 51.15561</td><td> 30.68533</td><td>13.296189</td><td> 97.67386</td></tr>\n",
       "\t<tr><td>92.514964</td><td>43.044644</td><td>58.160026</td><td>213.67204</td><td>15.475796</td><td>12.0773017</td><td>165.44104</td><td>137.07913</td><td>93.756598</td><td>30.164430</td><td>149.63861</td><td>41.408900</td><td>25.858668</td><td>49.919147</td><td>116.87010</td><td>15.617643</td><td>169.71626</td><td>112.54659</td><td>70.006151</td><td>326.91525</td></tr>\n",
       "\t<tr><td>28.906191</td><td>14.392538</td><td>22.185618</td><td> 67.16977</td><td> 6.145258</td><td> 3.6924770</td><td> 64.94513</td><td> 55.21035</td><td>30.688892</td><td>10.176825</td><td> 45.25853</td><td>12.749376</td><td> 9.032793</td><td>15.495374</td><td> 36.81719</td><td> 5.123724</td><td> 58.67368</td><td> 29.58193</td><td>21.623532</td><td> 99.93379</td></tr>\n",
       "\t<tr><td> 9.551062</td><td> 3.410251</td><td> 4.878865</td><td> 18.12767</td><td> 1.614065</td><td> 0.8931854</td><td> 17.28203</td><td> 17.22031</td><td> 8.142591</td><td> 3.083274</td><td> 12.23204</td><td> 3.798106</td><td> 2.193490</td><td> 4.518360</td><td> 11.77150</td><td> 1.443103</td><td> 15.93234</td><td> 10.51158</td><td> 4.555954</td><td> 31.80592</td></tr>\n",
       "\t<tr><td>33.199312</td><td>15.425443</td><td>17.890821</td><td> 63.44911</td><td> 6.417212</td><td> 4.1225359</td><td> 74.37597</td><td> 55.93898</td><td>28.053037</td><td>13.419521</td><td> 47.36161</td><td>14.367968</td><td> 8.493563</td><td>13.115915</td><td> 40.56524</td><td> 5.958505</td><td> 64.48142</td><td> 39.18703</td><td>21.834745</td><td>128.40462</td></tr>\n",
       "\t<tr><td>63.756096</td><td>29.617267</td><td>34.687515</td><td>133.08928</td><td> 8.728729</td><td> 6.6314772</td><td>143.08539</td><td> 85.97149</td><td>57.780871</td><td>21.128811</td><td> 88.68438</td><td>20.690290</td><td>17.377212</td><td>30.777911</td><td> 82.98544</td><td> 8.092405</td><td>106.46163</td><td> 68.40326</td><td>37.420694</td><td>216.26881</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 10 × 20 of type dbl\n",
       "\\begin{tabular}{llllllllllllllllllll}\n",
       "\t 19.575269 &  9.736831 & 11.918005 &  45.99760 &  3.648907 &  2.5784951 &  34.45140 &  33.24528 & 18.493514 &  7.586944 &  34.47187 &  8.502502 &  4.789169 &  8.425440 &  25.32743 &  3.844023 &  35.53169 &  27.05855 & 11.721207 &  60.34530\\\\\n",
       "\t 12.070171 &  6.131606 &  8.046342 &  28.11796 &  2.466564 &  1.3273041 &  31.93427 &  19.18484 & 15.184394 &  5.087324 &  18.22168 &  5.176799 &  3.592614 &  5.859558 &  17.07175 &  2.074506 &  24.11629 &  13.17105 &  7.189315 &  45.39134\\\\\n",
       "\t 50.718936 & 25.271544 & 33.163816 & 105.56265 &  8.707644 &  5.6133327 & 111.64650 &  80.88702 & 52.891471 & 20.047222 &  87.08436 & 21.658894 & 13.387657 & 23.198969 &  68.64448 &  9.482063 &  90.73375 &  63.37555 & 31.235192 & 191.66588\\\\\n",
       "\t 24.850634 & 12.467870 & 14.588572 &  51.26094 &  4.198930 &  3.1263918 &  57.58208 &  42.25084 & 24.161054 &  9.820436 &  47.76637 & 13.124946 &  6.436791 & 12.376809 &  33.32568 &  4.739224 &  51.69930 &  32.02140 & 16.057208 &  92.72647\\\\\n",
       "\t 26.243925 & 12.708966 & 18.181190 &  57.49178 &  4.373927 &  2.9283865 &  61.06450 &  51.11109 & 23.624734 &  9.403672 &  43.08685 & 11.952294 &  7.584674 & 12.718687 &  33.28133 &  4.426316 &  51.15561 &  30.68533 & 13.296189 &  97.67386\\\\\n",
       "\t 92.514964 & 43.044644 & 58.160026 & 213.67204 & 15.475796 & 12.0773017 & 165.44104 & 137.07913 & 93.756598 & 30.164430 & 149.63861 & 41.408900 & 25.858668 & 49.919147 & 116.87010 & 15.617643 & 169.71626 & 112.54659 & 70.006151 & 326.91525\\\\\n",
       "\t 28.906191 & 14.392538 & 22.185618 &  67.16977 &  6.145258 &  3.6924770 &  64.94513 &  55.21035 & 30.688892 & 10.176825 &  45.25853 & 12.749376 &  9.032793 & 15.495374 &  36.81719 &  5.123724 &  58.67368 &  29.58193 & 21.623532 &  99.93379\\\\\n",
       "\t  9.551062 &  3.410251 &  4.878865 &  18.12767 &  1.614065 &  0.8931854 &  17.28203 &  17.22031 &  8.142591 &  3.083274 &  12.23204 &  3.798106 &  2.193490 &  4.518360 &  11.77150 &  1.443103 &  15.93234 &  10.51158 &  4.555954 &  31.80592\\\\\n",
       "\t 33.199312 & 15.425443 & 17.890821 &  63.44911 &  6.417212 &  4.1225359 &  74.37597 &  55.93898 & 28.053037 & 13.419521 &  47.36161 & 14.367968 &  8.493563 & 13.115915 &  40.56524 &  5.958505 &  64.48142 &  39.18703 & 21.834745 & 128.40462\\\\\n",
       "\t 63.756096 & 29.617267 & 34.687515 & 133.08928 &  8.728729 &  6.6314772 & 143.08539 &  85.97149 & 57.780871 & 21.128811 &  88.68438 & 20.690290 & 17.377212 & 30.777911 &  82.98544 &  8.092405 & 106.46163 &  68.40326 & 37.420694 & 216.26881\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 10 × 20 of type dbl\n",
       "\n",
       "| 19.575269 |  9.736831 | 11.918005 |  45.99760 |  3.648907 |  2.5784951 |  34.45140 |  33.24528 | 18.493514 |  7.586944 |  34.47187 |  8.502502 |  4.789169 |  8.425440 |  25.32743 |  3.844023 |  35.53169 |  27.05855 | 11.721207 |  60.34530 |\n",
       "| 12.070171 |  6.131606 |  8.046342 |  28.11796 |  2.466564 |  1.3273041 |  31.93427 |  19.18484 | 15.184394 |  5.087324 |  18.22168 |  5.176799 |  3.592614 |  5.859558 |  17.07175 |  2.074506 |  24.11629 |  13.17105 |  7.189315 |  45.39134 |\n",
       "| 50.718936 | 25.271544 | 33.163816 | 105.56265 |  8.707644 |  5.6133327 | 111.64650 |  80.88702 | 52.891471 | 20.047222 |  87.08436 | 21.658894 | 13.387657 | 23.198969 |  68.64448 |  9.482063 |  90.73375 |  63.37555 | 31.235192 | 191.66588 |\n",
       "| 24.850634 | 12.467870 | 14.588572 |  51.26094 |  4.198930 |  3.1263918 |  57.58208 |  42.25084 | 24.161054 |  9.820436 |  47.76637 | 13.124946 |  6.436791 | 12.376809 |  33.32568 |  4.739224 |  51.69930 |  32.02140 | 16.057208 |  92.72647 |\n",
       "| 26.243925 | 12.708966 | 18.181190 |  57.49178 |  4.373927 |  2.9283865 |  61.06450 |  51.11109 | 23.624734 |  9.403672 |  43.08685 | 11.952294 |  7.584674 | 12.718687 |  33.28133 |  4.426316 |  51.15561 |  30.68533 | 13.296189 |  97.67386 |\n",
       "| 92.514964 | 43.044644 | 58.160026 | 213.67204 | 15.475796 | 12.0773017 | 165.44104 | 137.07913 | 93.756598 | 30.164430 | 149.63861 | 41.408900 | 25.858668 | 49.919147 | 116.87010 | 15.617643 | 169.71626 | 112.54659 | 70.006151 | 326.91525 |\n",
       "| 28.906191 | 14.392538 | 22.185618 |  67.16977 |  6.145258 |  3.6924770 |  64.94513 |  55.21035 | 30.688892 | 10.176825 |  45.25853 | 12.749376 |  9.032793 | 15.495374 |  36.81719 |  5.123724 |  58.67368 |  29.58193 | 21.623532 |  99.93379 |\n",
       "|  9.551062 |  3.410251 |  4.878865 |  18.12767 |  1.614065 |  0.8931854 |  17.28203 |  17.22031 |  8.142591 |  3.083274 |  12.23204 |  3.798106 |  2.193490 |  4.518360 |  11.77150 |  1.443103 |  15.93234 |  10.51158 |  4.555954 |  31.80592 |\n",
       "| 33.199312 | 15.425443 | 17.890821 |  63.44911 |  6.417212 |  4.1225359 |  74.37597 |  55.93898 | 28.053037 | 13.419521 |  47.36161 | 14.367968 |  8.493563 | 13.115915 |  40.56524 |  5.958505 |  64.48142 |  39.18703 | 21.834745 | 128.40462 |\n",
       "| 63.756096 | 29.617267 | 34.687515 | 133.08928 |  8.728729 |  6.6314772 | 143.08539 |  85.97149 | 57.780871 | 21.128811 |  88.68438 | 20.690290 | 17.377212 | 30.777911 |  82.98544 |  8.092405 | 106.46163 |  68.40326 | 37.420694 | 216.26881 |\n",
       "\n"
      ],
      "text/plain": [
       "      [,1]      [,2]      [,3]      [,4]      [,5]      [,6]       [,7]     \n",
       " [1,] 19.575269  9.736831 11.918005  45.99760  3.648907  2.5784951  34.45140\n",
       " [2,] 12.070171  6.131606  8.046342  28.11796  2.466564  1.3273041  31.93427\n",
       " [3,] 50.718936 25.271544 33.163816 105.56265  8.707644  5.6133327 111.64650\n",
       " [4,] 24.850634 12.467870 14.588572  51.26094  4.198930  3.1263918  57.58208\n",
       " [5,] 26.243925 12.708966 18.181190  57.49178  4.373927  2.9283865  61.06450\n",
       " [6,] 92.514964 43.044644 58.160026 213.67204 15.475796 12.0773017 165.44104\n",
       " [7,] 28.906191 14.392538 22.185618  67.16977  6.145258  3.6924770  64.94513\n",
       " [8,]  9.551062  3.410251  4.878865  18.12767  1.614065  0.8931854  17.28203\n",
       " [9,] 33.199312 15.425443 17.890821  63.44911  6.417212  4.1225359  74.37597\n",
       "[10,] 63.756096 29.617267 34.687515 133.08928  8.728729  6.6314772 143.08539\n",
       "      [,8]      [,9]      [,10]     [,11]     [,12]     [,13]     [,14]    \n",
       " [1,]  33.24528 18.493514  7.586944  34.47187  8.502502  4.789169  8.425440\n",
       " [2,]  19.18484 15.184394  5.087324  18.22168  5.176799  3.592614  5.859558\n",
       " [3,]  80.88702 52.891471 20.047222  87.08436 21.658894 13.387657 23.198969\n",
       " [4,]  42.25084 24.161054  9.820436  47.76637 13.124946  6.436791 12.376809\n",
       " [5,]  51.11109 23.624734  9.403672  43.08685 11.952294  7.584674 12.718687\n",
       " [6,] 137.07913 93.756598 30.164430 149.63861 41.408900 25.858668 49.919147\n",
       " [7,]  55.21035 30.688892 10.176825  45.25853 12.749376  9.032793 15.495374\n",
       " [8,]  17.22031  8.142591  3.083274  12.23204  3.798106  2.193490  4.518360\n",
       " [9,]  55.93898 28.053037 13.419521  47.36161 14.367968  8.493563 13.115915\n",
       "[10,]  85.97149 57.780871 21.128811  88.68438 20.690290 17.377212 30.777911\n",
       "      [,15]     [,16]     [,17]     [,18]     [,19]     [,20]    \n",
       " [1,]  25.32743  3.844023  35.53169  27.05855 11.721207  60.34530\n",
       " [2,]  17.07175  2.074506  24.11629  13.17105  7.189315  45.39134\n",
       " [3,]  68.64448  9.482063  90.73375  63.37555 31.235192 191.66588\n",
       " [4,]  33.32568  4.739224  51.69930  32.02140 16.057208  92.72647\n",
       " [5,]  33.28133  4.426316  51.15561  30.68533 13.296189  97.67386\n",
       " [6,] 116.87010 15.617643 169.71626 112.54659 70.006151 326.91525\n",
       " [7,]  36.81719  5.123724  58.67368  29.58193 21.623532  99.93379\n",
       " [8,]  11.77150  1.443103  15.93234  10.51158  4.555954  31.80592\n",
       " [9,]  40.56524  5.958505  64.48142  39.18703 21.834745 128.40462\n",
       "[10,]  82.98544  8.092405 106.46163  68.40326 37.420694 216.26881"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rt[1:3,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: rjags\n",
      "\n",
      "Loading required package: coda\n",
      "\n",
      "Linked to JAGS 4.3.0\n",
      "\n",
      "Loaded modules: basemod,bugs\n",
      "\n",
      "\n",
      "Attaching package: ‘R2jags’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:coda’:\n",
      "\n",
      "    traceplot\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(R2jags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "data <- list(N, I, rt)\n",
    "names(data) <- c(\"N\", \"I\", \"rt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "model <- function() {\n",
    "    for (n in 1:N){ \n",
    "        for (i in 1:I){\n",
    "            rt[n,i] ~ dlnorm(time_intense[i]-tau[n],(1/time_discrm_inv[i])^2)\n",
    "        }\n",
    "        tau[n] ~ dnorm(0,1)\n",
    "    }\n",
    "for (i in 1:I){\n",
    "    time_intense[i] ~ dnorm(3,1)\n",
    "    time_discrm_inv[i] ~ dgamma(1,1)\n",
    "    time_discrm[i] <- inverse(time_discrm_inv[i])\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "module glm loaded\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling model graph\n",
      "   Resolving undeclared variables\n",
      "   Allocating nodes\n",
      "Graph information:\n",
      "   Observed stochastic nodes: 10000\n",
      "   Unobserved stochastic nodes: 540\n",
      "   Total graph size: 20606\n",
      "\n",
      "Initializing model\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sim <- jags(data,\n",
    "            parameters.to.save = c(\"tau\", \"time_intense\", \"time_discrm\"),\n",
    "            model.file = model,\n",
    "            n.iter = 4000,\n",
    "            n.burnin = 2000,\n",
    "            n.chains = 2,\n",
    "            progress.bar = \"text\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Bugs model at \"/tmp/Rtmp9w3egq/model24ec1ddfddf.txt\", fit using jags,\n",
      " 2 chains, each with 4000 iterations (first 2000 discarded), n.thin = 2\n",
      " n.sims = 2000 iterations saved\n",
      "                   mu.vect sd.vect      2.5%       25%       50%       75%\n",
      "tau[1]               0.411   0.044     0.347     0.378     0.402     0.443\n",
      "tau[2]               0.854   0.043     0.787     0.822     0.844     0.884\n",
      "tau[3]              -0.548   0.044    -0.614    -0.581    -0.558    -0.516\n",
      "tau[4]               0.132   0.044     0.065     0.099     0.121     0.165\n",
      "tau[5]               0.118   0.044     0.050     0.084     0.109     0.148\n",
      "tau[6]              -1.141   0.044    -1.207    -1.174    -1.151    -1.110\n",
      "tau[7]              -0.031   0.045    -0.099    -0.064    -0.041     0.001\n",
      "tau[8]               1.258   0.043     1.190     1.226     1.249     1.290\n",
      "tau[9]              -0.094   0.044    -0.160    -0.126    -0.103    -0.060\n",
      "tau[10]             -0.666   0.044    -0.733    -0.700    -0.675    -0.634\n",
      "tau[11]             -0.391   0.044    -0.458    -0.423    -0.401    -0.360\n",
      "tau[12]             -0.556   0.044    -0.623    -0.589    -0.566    -0.525\n",
      "tau[13]             -0.302   0.043    -0.367    -0.334    -0.311    -0.272\n",
      "tau[14]              0.352   0.045     0.284     0.318     0.342     0.385\n",
      "tau[15]             -0.356   0.044    -0.422    -0.389    -0.365    -0.325\n",
      "tau[16]             -0.154   0.044    -0.221    -0.187    -0.164    -0.123\n",
      "tau[17]             -0.644   0.044    -0.713    -0.676    -0.652    -0.614\n",
      "tau[18]              0.393   0.044     0.326     0.360     0.384     0.424\n",
      "tau[19]              0.404   0.044     0.339     0.371     0.395     0.436\n",
      "tau[20]             -1.060   0.044    -1.127    -1.093    -1.070    -1.029\n",
      "tau[21]             -0.139   0.044    -0.206    -0.172    -0.148    -0.108\n",
      "tau[22]             -0.514   0.044    -0.583    -0.546    -0.523    -0.483\n",
      "tau[23]              1.171   0.043     1.105     1.138     1.162     1.203\n",
      "tau[24]              0.947   0.044     0.881     0.913     0.936     0.978\n",
      "tau[25]             -1.800   0.044    -1.867    -1.833    -1.809    -1.770\n",
      "tau[26]             -1.956   0.044    -2.020    -1.989    -1.965    -1.925\n",
      "tau[27]              0.776   0.044     0.708     0.742     0.767     0.807\n",
      "tau[28]              0.812   0.044     0.745     0.778     0.801     0.845\n",
      "tau[29]             -0.054   0.044    -0.123    -0.087    -0.064    -0.021\n",
      "tau[30]             -0.862   0.044    -0.929    -0.895    -0.874    -0.829\n",
      "tau[31]              0.453   0.044     0.386     0.420     0.443     0.483\n",
      "tau[32]             -1.039   0.044    -1.107    -1.071    -1.049    -1.009\n",
      "tau[33]              0.584   0.044     0.514     0.550     0.573     0.613\n",
      "tau[34]              0.657   0.044     0.590     0.624     0.649     0.687\n",
      "tau[35]             -0.651   0.044    -0.718    -0.684    -0.661    -0.619\n",
      "tau[36]             -0.469   0.044    -0.537    -0.503    -0.479    -0.437\n",
      "tau[37]              0.091   0.044     0.023     0.059     0.080     0.121\n",
      "tau[38]             -0.148   0.045    -0.216    -0.181    -0.156    -0.115\n",
      "tau[39]              0.089   0.043     0.022     0.057     0.079     0.119\n",
      "tau[40]              0.830   0.044     0.764     0.797     0.820     0.861\n",
      "tau[41]              0.656   0.044     0.589     0.623     0.647     0.687\n",
      "tau[42]             -0.899   0.044    -0.968    -0.931    -0.909    -0.867\n",
      "tau[43]             -0.811   0.044    -0.876    -0.844    -0.820    -0.780\n",
      "tau[44]              1.357   0.044     1.291     1.323     1.347     1.389\n",
      "tau[45]              0.780   0.044     0.713     0.747     0.771     0.813\n",
      "tau[46]             -0.558   0.044    -0.629    -0.591    -0.567    -0.527\n",
      "tau[47]              0.124   0.044     0.058     0.091     0.113     0.156\n",
      "tau[48]             -0.969   0.044    -1.036    -1.002    -0.978    -0.938\n",
      "tau[49]             -0.679   0.043    -0.744    -0.713    -0.689    -0.649\n",
      "tau[50]              0.077   0.044     0.010     0.045     0.069     0.106\n",
      "tau[51]             -1.836   0.044    -1.903    -1.869    -1.846    -1.802\n",
      "tau[52]             -1.274   0.044    -1.341    -1.307    -1.285    -1.243\n",
      "tau[53]             -1.045   0.044    -1.111    -1.077    -1.054    -1.013\n",
      "tau[54]             -2.437   0.043    -2.504    -2.469    -2.445    -2.406\n",
      "tau[55]              0.973   0.044     0.905     0.940     0.964     1.004\n",
      "tau[56]              0.312   0.043     0.244     0.279     0.304     0.342\n",
      "tau[57]             -0.319   0.043    -0.386    -0.353    -0.327    -0.290\n",
      "tau[58]             -0.146   0.044    -0.213    -0.180    -0.157    -0.117\n",
      "tau[59]             -0.463   0.043    -0.531    -0.496    -0.472    -0.432\n",
      "tau[60]              1.002   0.044     0.932     0.969     0.992     1.033\n",
      "tau[61]             -0.834   0.044    -0.901    -0.866    -0.843    -0.802\n",
      "tau[62]             -1.211   0.044    -1.278    -1.244    -1.220    -1.180\n",
      "tau[63]              0.349   0.044     0.283     0.316     0.338     0.380\n",
      "tau[64]             -0.098   0.044    -0.165    -0.131    -0.106    -0.067\n",
      "tau[65]              0.638   0.044     0.570     0.605     0.629     0.669\n",
      "tau[66]              0.280   0.045     0.210     0.247     0.271     0.312\n",
      "tau[67]             -0.820   0.043    -0.886    -0.853    -0.830    -0.788\n",
      "tau[68]             -0.502   0.043    -0.568    -0.536    -0.511    -0.473\n",
      "tau[69]              0.025   0.044    -0.043    -0.007     0.015     0.058\n",
      "tau[70]             -0.891   0.043    -0.957    -0.924    -0.899    -0.859\n",
      "tau[71]             -1.221   0.044    -1.287    -1.255    -1.231    -1.189\n",
      "tau[72]              0.713   0.043     0.646     0.680     0.704     0.744\n",
      "tau[73]              1.395   0.044     1.329     1.362     1.385     1.428\n",
      "tau[74]              0.375   0.044     0.307     0.342     0.367     0.407\n",
      "tau[75]             -1.381   0.045    -1.451    -1.415    -1.391    -1.350\n",
      "tau[76]             -0.836   0.044    -0.903    -0.869    -0.844    -0.805\n",
      "tau[77]             -0.031   0.044    -0.100    -0.063    -0.041     0.001\n",
      "tau[78]              1.302   0.044     1.233     1.270     1.293     1.333\n",
      "tau[79]              0.361   0.044     0.296     0.327     0.352     0.392\n",
      "tau[80]              0.161   0.043     0.095     0.128     0.151     0.193\n",
      "tau[81]             -1.631   0.043    -1.697    -1.663    -1.641    -1.599\n",
      "tau[82]             -1.214   0.044    -1.279    -1.248    -1.225    -1.183\n",
      "tau[83]             -0.672   0.043    -0.741    -0.705    -0.681    -0.641\n",
      "tau[84]              0.798   0.044     0.732     0.764     0.788     0.829\n",
      "tau[85]             -0.481   0.045    -0.550    -0.514    -0.491    -0.448\n",
      "tau[86]             -0.933   0.044    -1.000    -0.967    -0.943    -0.901\n",
      "tau[87]             -2.870   0.045    -2.939    -2.904    -2.880    -2.837\n",
      "tau[88]             -0.094   0.044    -0.161    -0.128    -0.104    -0.063\n",
      "tau[89]             -0.302   0.044    -0.370    -0.336    -0.312    -0.270\n",
      "tau[90]             -0.590   0.043    -0.657    -0.623    -0.600    -0.561\n",
      "tau[91]              0.217   0.043     0.150     0.184     0.208     0.247\n",
      "tau[92]             -0.822   0.044    -0.890    -0.856    -0.832    -0.791\n",
      "tau[93]             -0.170   0.043    -0.236    -0.201    -0.180    -0.140\n",
      "tau[94]             -0.093   0.044    -0.159    -0.126    -0.103    -0.061\n",
      "tau[95]              0.122   0.044     0.053     0.089     0.112     0.154\n",
      "tau[96]             -0.581   0.043    -0.648    -0.614    -0.590    -0.550\n",
      "tau[97]              0.554   0.044     0.489     0.520     0.544     0.586\n",
      "tau[98]             -0.986   0.044    -1.053    -1.019    -0.995    -0.955\n",
      "tau[99]              1.020   0.044     0.952     0.987     1.010     1.052\n",
      "tau[100]            -1.783   0.044    -1.853    -1.815    -1.794    -1.754\n",
      "tau[101]            -0.477   0.044    -0.546    -0.510    -0.487    -0.445\n",
      "tau[102]            -0.894   0.044    -0.964    -0.927    -0.902    -0.865\n",
      "tau[103]             0.435   0.043     0.369     0.402     0.427     0.467\n",
      "tau[104]             0.625   0.044     0.558     0.592     0.616     0.657\n",
      "tau[105]            -0.488   0.044    -0.554    -0.521    -0.497    -0.456\n",
      "tau[106]             0.336   0.043     0.269     0.303     0.326     0.366\n",
      "tau[107]             1.073   0.044     1.005     1.040     1.063     1.105\n",
      "tau[108]            -0.441   0.044    -0.509    -0.475    -0.452    -0.408\n",
      "tau[109]             2.274   0.044     2.207     2.241     2.265     2.306\n",
      "tau[110]             1.467   0.044     1.400     1.434     1.457     1.497\n",
      "tau[111]            -1.124   0.044    -1.191    -1.156    -1.133    -1.093\n",
      "tau[112]             0.876   0.043     0.812     0.844     0.867     0.905\n",
      "tau[113]             0.061   0.044    -0.004     0.026     0.051     0.093\n",
      "tau[114]            -0.121   0.044    -0.187    -0.155    -0.131    -0.089\n",
      "tau[115]             0.058   0.044    -0.013     0.024     0.049     0.089\n",
      "tau[116]             1.326   0.044     1.260     1.292     1.317     1.359\n",
      "tau[117]             0.380   0.043     0.315     0.348     0.370     0.408\n",
      "tau[118]             0.369   0.043     0.303     0.336     0.359     0.398\n",
      "tau[119]             0.566   0.044     0.500     0.533     0.558     0.598\n",
      "tau[120]            -0.152   0.043    -0.217    -0.185    -0.161    -0.121\n",
      "tau[121]             0.365   0.044     0.297     0.331     0.355     0.396\n",
      "tau[122]             0.007   0.044    -0.059    -0.027    -0.003     0.038\n",
      "tau[123]             1.375   0.044     1.306     1.341     1.365     1.406\n",
      "tau[124]            -2.025   0.044    -2.096    -2.058    -2.034    -1.994\n",
      "tau[125]             0.812   0.044     0.745     0.780     0.802     0.845\n",
      "tau[126]            -0.050   0.043    -0.115    -0.082    -0.058    -0.019\n",
      "tau[127]             1.056   0.044     0.990     1.022     1.046     1.085\n",
      "tau[128]             0.029   0.043    -0.039    -0.004     0.020     0.060\n",
      "tau[129]            -0.232   0.044    -0.298    -0.265    -0.241    -0.200\n",
      "tau[130]            -1.794   0.044    -1.861    -1.826    -1.804    -1.763\n",
      "tau[131]            -0.928   0.044    -0.996    -0.961    -0.939    -0.896\n",
      "tau[132]            -0.169   0.043    -0.237    -0.202    -0.178    -0.138\n",
      "tau[133]             0.249   0.044     0.183     0.216     0.239     0.280\n",
      "tau[134]            -0.957   0.044    -1.024    -0.990    -0.966    -0.927\n",
      "tau[135]            -0.975   0.044    -1.042    -1.008    -0.984    -0.944\n",
      "tau[136]            -1.341   0.044    -1.410    -1.373    -1.351    -1.311\n",
      "tau[137]             0.371   0.044     0.302     0.338     0.361     0.402\n",
      "tau[138]            -1.521   0.044    -1.585    -1.554    -1.531    -1.489\n",
      "tau[139]            -0.558   0.044    -0.625    -0.591    -0.567    -0.526\n",
      "tau[140]             0.029   0.044    -0.039    -0.003     0.020     0.061\n",
      "tau[141]            -1.087   0.043    -1.154    -1.120    -1.096    -1.057\n",
      "tau[142]             0.906   0.044     0.841     0.872     0.897     0.935\n",
      "tau[143]            -1.954   0.043    -2.019    -1.987    -1.963    -1.923\n",
      "tau[144]             0.010   0.045    -0.058    -0.024     0.001     0.042\n",
      "tau[145]            -1.847   0.044    -1.914    -1.879    -1.856    -1.815\n",
      "tau[146]             1.801   0.043     1.739     1.768     1.791     1.832\n",
      "tau[147]            -0.337   0.044    -0.405    -0.370    -0.346    -0.306\n",
      "tau[148]            -0.424   0.044    -0.493    -0.456    -0.434    -0.393\n",
      "tau[149]            -0.027   0.043    -0.093    -0.060    -0.038     0.004\n",
      "tau[150]             0.195   0.044     0.126     0.162     0.186     0.226\n",
      "tau[151]             0.491   0.044     0.425     0.458     0.481     0.524\n",
      "tau[152]             0.485   0.044     0.417     0.454     0.475     0.514\n",
      "tau[153]            -0.326   0.043    -0.393    -0.359    -0.336    -0.295\n",
      "tau[154]            -0.372   0.044    -0.439    -0.405    -0.383    -0.342\n",
      "tau[155]            -1.824   0.044    -1.892    -1.857    -1.834    -1.794\n",
      "tau[156]            -1.084   0.043    -1.151    -1.116    -1.094    -1.052\n",
      "tau[157]             0.286   0.044     0.218     0.253     0.275     0.317\n",
      "tau[158]             0.884   0.044     0.817     0.850     0.874     0.915\n",
      "tau[159]             0.549   0.044     0.483     0.515     0.541     0.580\n",
      "tau[160]             0.057   0.044    -0.012     0.024     0.047     0.088\n",
      "tau[161]            -1.050   0.043    -1.115    -1.084    -1.059    -1.018\n",
      "tau[162]             0.321   0.044     0.256     0.288     0.311     0.351\n",
      "tau[163]             0.335   0.043     0.269     0.302     0.326     0.365\n",
      "tau[164]             0.243   0.044     0.177     0.209     0.233     0.274\n",
      "tau[165]            -0.213   0.044    -0.278    -0.247    -0.222    -0.183\n",
      "tau[166]            -1.361   0.044    -1.431    -1.395    -1.370    -1.330\n",
      "tau[167]             0.069   0.044     0.003     0.036     0.060     0.101\n",
      "tau[168]             0.474   0.044     0.408     0.441     0.465     0.505\n",
      "tau[169]             0.038   0.044    -0.029     0.005     0.028     0.070\n",
      "tau[170]            -0.858   0.043    -0.923    -0.890    -0.868    -0.827\n",
      "tau[171]            -0.533   0.043    -0.601    -0.566    -0.543    -0.502\n",
      "tau[172]            -0.251   0.044    -0.318    -0.285    -0.261    -0.220\n",
      "tau[173]            -0.724   0.043    -0.789    -0.758    -0.734    -0.694\n",
      "tau[174]             0.317   0.044     0.250     0.284     0.307     0.349\n",
      "tau[175]            -0.306   0.044    -0.373    -0.340    -0.315    -0.275\n",
      "tau[176]            -0.154   0.043    -0.221    -0.187    -0.162    -0.123\n",
      "tau[177]             0.284   0.043     0.216     0.252     0.275     0.316\n",
      "tau[178]            -1.655   0.044    -1.720    -1.688    -1.665    -1.625\n",
      "tau[179]            -0.914   0.044    -0.979    -0.947    -0.924    -0.881\n",
      "tau[180]            -1.355   0.044    -1.420    -1.388    -1.364    -1.321\n",
      "tau[181]            -0.483   0.044    -0.551    -0.516    -0.492    -0.455\n",
      "tau[182]            -0.984   0.045    -1.053    -1.018    -0.993    -0.950\n",
      "tau[183]             1.116   0.044     1.045     1.083     1.107     1.148\n",
      "tau[184]             0.494   0.044     0.427     0.461     0.484     0.523\n",
      "tau[185]             1.974   0.045     1.906     1.939     1.964     2.006\n",
      "tau[186]             0.146   0.044     0.078     0.113     0.136     0.179\n",
      "tau[187]             0.101   0.043     0.035     0.068     0.092     0.131\n",
      "tau[188]             1.764   0.044     1.698     1.731     1.755     1.795\n",
      "tau[189]             0.430   0.043     0.365     0.397     0.421     0.460\n",
      "tau[190]             1.409   0.044     1.341     1.375     1.400     1.439\n",
      "tau[191]             1.238   0.044     1.172     1.205     1.229     1.267\n",
      "tau[192]             0.473   0.044     0.408     0.439     0.463     0.504\n",
      "tau[193]            -0.297   0.044    -0.362    -0.330    -0.307    -0.265\n",
      "tau[194]            -0.981   0.045    -1.049    -1.014    -0.991    -0.947\n",
      "tau[195]             1.107   0.043     1.039     1.074     1.097     1.140\n",
      "tau[196]            -0.319   0.043    -0.383    -0.353    -0.329    -0.288\n",
      "tau[197]            -0.699   0.044    -0.766    -0.732    -0.709    -0.669\n",
      "tau[198]             0.453   0.044     0.385     0.419     0.443     0.485\n",
      "tau[199]             0.666   0.044     0.599     0.633     0.657     0.698\n",
      "tau[200]            -0.762   0.044    -0.829    -0.795    -0.772    -0.730\n",
      "tau[201]            -1.243   0.045    -1.311    -1.278    -1.253    -1.211\n",
      "tau[202]             0.376   0.044     0.308     0.343     0.367     0.407\n",
      "tau[203]             0.451   0.044     0.383     0.419     0.442     0.481\n",
      "tau[204]            -0.872   0.045    -0.941    -0.905    -0.881    -0.838\n",
      "tau[205]            -0.613   0.043    -0.680    -0.645    -0.622    -0.585\n",
      "tau[206]            -1.450   0.044    -1.517    -1.483    -1.459    -1.419\n",
      "tau[207]            -0.681   0.044    -0.747    -0.713    -0.691    -0.649\n",
      "tau[208]             0.003   0.043    -0.063    -0.029    -0.005     0.033\n",
      "tau[209]            -1.500   0.045    -1.570    -1.534    -1.509    -1.467\n",
      "tau[210]            -0.928   0.044    -0.997    -0.962    -0.938    -0.897\n",
      "tau[211]            -0.469   0.044    -0.536    -0.502    -0.479    -0.438\n",
      "tau[212]            -2.358   0.045    -2.428    -2.392    -2.367    -2.326\n",
      "tau[213]             0.719   0.044     0.652     0.685     0.709     0.750\n",
      "tau[214]             0.598   0.043     0.531     0.565     0.589     0.631\n",
      "tau[215]            -0.064   0.045    -0.132    -0.098    -0.074    -0.033\n",
      "tau[216]             0.499   0.044     0.431     0.465     0.489     0.530\n",
      "tau[217]            -0.627   0.044    -0.693    -0.660    -0.637    -0.596\n",
      "tau[218]            -1.568   0.044    -1.636    -1.602    -1.578    -1.537\n",
      "tau[219]             0.095   0.044     0.027     0.062     0.086     0.127\n",
      "tau[220]             1.260   0.044     1.193     1.227     1.249     1.291\n",
      "tau[221]             1.778   0.043     1.711     1.746     1.769     1.807\n",
      "tau[222]             2.796   0.044     2.729     2.763     2.787     2.828\n",
      "tau[223]             0.875   0.043     0.810     0.843     0.865     0.905\n",
      "tau[224]            -0.725   0.044    -0.793    -0.758    -0.733    -0.693\n",
      "tau[225]             0.466   0.044     0.398     0.432     0.457     0.495\n",
      "tau[226]             0.295   0.044     0.227     0.263     0.286     0.326\n",
      "tau[227]            -0.406   0.044    -0.475    -0.438    -0.415    -0.375\n",
      "tau[228]             1.404   0.043     1.336     1.373     1.394     1.432\n",
      "tau[229]            -0.618   0.044    -0.685    -0.652    -0.628    -0.586\n",
      "tau[230]            -0.123   0.045    -0.192    -0.157    -0.132    -0.090\n",
      "tau[231]            -0.392   0.044    -0.462    -0.426    -0.401    -0.360\n",
      "tau[232]            -1.345   0.044    -1.413    -1.379    -1.353    -1.316\n",
      "tau[233]            -1.061   0.043    -1.128    -1.094    -1.071    -1.029\n",
      "tau[234]             1.021   0.043     0.956     0.988     1.012     1.051\n",
      "tau[235]            -0.064   0.044    -0.133    -0.097    -0.072    -0.034\n",
      "tau[236]            -1.455   0.044    -1.521    -1.489    -1.464    -1.423\n",
      "tau[237]            -0.606   0.044    -0.674    -0.639    -0.616    -0.574\n",
      "tau[238]             0.757   0.044     0.690     0.723     0.747     0.787\n",
      "tau[239]             0.801   0.044     0.733     0.769     0.792     0.831\n",
      "tau[240]             1.082   0.044     1.013     1.049     1.073     1.113\n",
      "tau[241]             1.309   0.043     1.241     1.277     1.300     1.337\n",
      "tau[242]             0.290   0.044     0.221     0.256     0.280     0.321\n",
      "tau[243]             1.505   0.044     1.438     1.472     1.496     1.533\n",
      "tau[244]             0.738   0.044     0.670     0.706     0.729     0.770\n",
      "tau[245]            -0.087   0.044    -0.153    -0.120    -0.096    -0.055\n",
      "tau[246]            -0.554   0.043    -0.621    -0.587    -0.563    -0.523\n",
      "tau[247]            -0.976   0.044    -1.044    -1.008    -0.985    -0.944\n",
      "tau[248]             2.345   0.044     2.278     2.312     2.337     2.374\n",
      "tau[249]             0.663   0.044     0.595     0.630     0.654     0.696\n",
      "tau[250]             0.227   0.044     0.161     0.195     0.218     0.258\n",
      "tau[251]             1.975   0.043     1.907     1.942     1.965     2.006\n",
      "tau[252]             0.153   0.044     0.086     0.120     0.143     0.184\n",
      "tau[253]             0.009   0.043    -0.057    -0.024     0.000     0.039\n",
      "tau[254]            -1.059   0.044    -1.125    -1.092    -1.068    -1.027\n",
      "tau[255]            -0.812   0.043    -0.877    -0.843    -0.821    -0.782\n",
      "tau[256]             0.023   0.043    -0.042    -0.009     0.012     0.052\n",
      "tau[257]            -1.297   0.043    -1.363    -1.330    -1.307    -1.266\n",
      "tau[258]            -0.062   0.045    -0.129    -0.096    -0.073    -0.028\n",
      "tau[259]             0.049   0.044    -0.019     0.016     0.040     0.081\n",
      "tau[260]            -2.968   0.044    -3.037    -3.001    -2.978    -2.939\n",
      "tau[261]            -1.809   0.044    -1.878    -1.842    -1.818    -1.778\n",
      "tau[262]            -0.233   0.044    -0.302    -0.266    -0.242    -0.199\n",
      "tau[263]            -0.015   0.044    -0.083    -0.048    -0.025     0.015\n",
      "tau[264]             2.781   0.044     2.715     2.747     2.771     2.812\n",
      "tau[265]             0.100   0.044     0.031     0.067     0.092     0.132\n",
      "tau[266]            -1.029   0.043    -1.095    -1.062    -1.039    -0.997\n",
      "tau[267]             0.196   0.044     0.128     0.163     0.187     0.227\n",
      "tau[268]             0.269   0.044     0.200     0.236     0.259     0.299\n",
      "tau[269]             3.182   0.043     3.113     3.149     3.173     3.213\n",
      "tau[270]             0.115   0.044     0.049     0.083     0.107     0.144\n",
      "tau[271]             0.840   0.044     0.774     0.807     0.830     0.870\n",
      "tau[272]             0.082   0.043     0.019     0.050     0.073     0.114\n",
      "tau[273]            -0.214   0.044    -0.280    -0.248    -0.224    -0.182\n",
      "tau[274]             0.014   0.044    -0.051    -0.020     0.004     0.046\n",
      "tau[275]            -0.829   0.044    -0.898    -0.862    -0.839    -0.798\n",
      "tau[276]            -1.541   0.044    -1.607    -1.574    -1.550    -1.511\n",
      "tau[277]            -0.669   0.043    -0.737    -0.702    -0.677    -0.638\n",
      "tau[278]            -1.107   0.043    -1.177    -1.140    -1.116    -1.076\n",
      "tau[279]             1.047   0.044     0.980     1.015     1.037     1.079\n",
      "tau[280]            -0.962   0.044    -1.028    -0.995    -0.971    -0.931\n",
      "tau[281]             0.256   0.044     0.189     0.224     0.246     0.287\n",
      "tau[282]            -0.500   0.044    -0.566    -0.534    -0.510    -0.469\n",
      "tau[283]             0.053   0.043    -0.014     0.021     0.043     0.084\n",
      "tau[284]            -0.378   0.043    -0.444    -0.410    -0.387    -0.348\n",
      "tau[285]             0.464   0.044     0.394     0.432     0.455     0.495\n",
      "tau[286]            -1.190   0.044    -1.256    -1.223    -1.198    -1.158\n",
      "tau[287]             0.739   0.045     0.672     0.706     0.730     0.772\n",
      "tau[288]             1.484   0.044     1.417     1.451     1.474     1.515\n",
      "tau[289]             0.504   0.044     0.437     0.472     0.495     0.536\n",
      "tau[290]             0.948   0.044     0.880     0.916     0.939     0.978\n",
      "tau[291]             1.001   0.044     0.933     0.968     0.991     1.032\n",
      "tau[292]            -0.448   0.044    -0.517    -0.480    -0.458    -0.418\n",
      "tau[293]             1.830   0.044     1.760     1.796     1.820     1.862\n",
      "tau[294]             0.593   0.044     0.525     0.561     0.583     0.623\n",
      "tau[295]             1.447   0.044     1.381     1.414     1.438     1.478\n",
      "tau[296]            -0.099   0.044    -0.166    -0.132    -0.109    -0.070\n",
      "tau[297]             0.325   0.044     0.257     0.293     0.315     0.357\n",
      "tau[298]            -1.056   0.044    -1.123    -1.088    -1.066    -1.025\n",
      "tau[299]             0.567   0.044     0.501     0.534     0.558     0.597\n",
      "tau[300]             0.152   0.044     0.086     0.118     0.142     0.184\n",
      "tau[301]             0.820   0.043     0.751     0.787     0.812     0.849\n",
      "tau[302]            -0.953   0.043    -1.020    -0.985    -0.961    -0.923\n",
      "tau[303]            -1.364   0.043    -1.430    -1.397    -1.374    -1.333\n",
      "tau[304]             1.209   0.043     1.144     1.176     1.199     1.238\n",
      "tau[305]            -1.424   0.044    -1.489    -1.458    -1.435    -1.391\n",
      "tau[306]             0.191   0.044     0.124     0.158     0.181     0.221\n",
      "tau[307]            -0.208   0.044    -0.275    -0.240    -0.218    -0.176\n",
      "tau[308]            -2.746   0.044    -2.816    -2.780    -2.756    -2.715\n",
      "tau[309]            -0.753   0.044    -0.819    -0.786    -0.764    -0.722\n",
      "tau[310]             0.455   0.043     0.390     0.422     0.445     0.484\n",
      "tau[311]            -0.130   0.044    -0.198    -0.163    -0.138    -0.096\n",
      "tau[312]            -0.422   0.043    -0.489    -0.455    -0.432    -0.392\n",
      "tau[313]             0.299   0.043     0.231     0.267     0.290     0.329\n",
      "tau[314]            -0.248   0.044    -0.317    -0.281    -0.257    -0.217\n",
      "tau[315]             1.067   0.044     1.002     1.034     1.058     1.097\n",
      "tau[316]            -0.670   0.044    -0.738    -0.704    -0.680    -0.639\n",
      "tau[317]             2.560   0.044     2.493     2.527     2.549     2.591\n",
      "tau[318]            -1.460   0.044    -1.528    -1.494    -1.469    -1.429\n",
      "tau[319]            -0.208   0.044    -0.276    -0.241    -0.216    -0.177\n",
      "tau[320]            -2.105   0.044    -2.173    -2.138    -2.115    -2.074\n",
      "tau[321]             0.289   0.043     0.220     0.256     0.280     0.319\n",
      "tau[322]             0.678   0.043     0.612     0.646     0.669     0.708\n",
      "tau[323]             0.172   0.043     0.104     0.139     0.164     0.203\n",
      "tau[324]             0.495   0.045     0.426     0.461     0.485     0.526\n",
      "tau[325]             0.283   0.044     0.215     0.250     0.273     0.315\n",
      "tau[326]             0.038   0.044    -0.029     0.005     0.029     0.069\n",
      "tau[327]             0.614   0.045     0.545     0.579     0.603     0.647\n",
      "tau[328]            -1.046   0.044    -1.113    -1.078    -1.055    -1.016\n",
      "tau[329]             0.012   0.044    -0.055    -0.020     0.003     0.043\n",
      "tau[330]            -0.542   0.044    -0.606    -0.575    -0.552    -0.512\n",
      "tau[331]             1.110   0.044     1.042     1.076     1.101     1.141\n",
      "tau[332]             2.320   0.043     2.257     2.287     2.310     2.351\n",
      "tau[333]             1.218   0.044     1.153     1.186     1.209     1.249\n",
      "tau[334]            -0.010   0.044    -0.077    -0.043    -0.019     0.018\n",
      "tau[335]             0.828   0.044     0.762     0.796     0.818     0.858\n",
      "tau[336]            -0.633   0.044    -0.700    -0.667    -0.643    -0.602\n",
      "tau[337]            -0.423   0.044    -0.490    -0.456    -0.433    -0.391\n",
      "tau[338]            -0.946   0.045    -1.014    -0.980    -0.956    -0.914\n",
      "tau[339]             0.594   0.044     0.526     0.561     0.585     0.625\n",
      "tau[340]             1.430   0.043     1.364     1.397     1.420     1.461\n",
      "tau[341]            -0.115   0.044    -0.181    -0.148    -0.125    -0.084\n",
      "tau[342]            -0.897   0.044    -0.964    -0.931    -0.907    -0.865\n",
      "tau[343]            -2.357   0.044    -2.423    -2.389    -2.367    -2.325\n",
      "tau[344]             0.315   0.044     0.246     0.283     0.307     0.347\n",
      "tau[345]            -0.636   0.044    -0.703    -0.669    -0.646    -0.605\n",
      "tau[346]             0.133   0.044     0.062     0.099     0.124     0.165\n",
      "tau[347]             0.708   0.044     0.642     0.675     0.698     0.738\n",
      "tau[348]            -1.838   0.044    -1.907    -1.872    -1.847    -1.808\n",
      "tau[349]            -0.896   0.044    -0.960    -0.927    -0.905    -0.864\n",
      "tau[350]             0.050   0.044    -0.018     0.016     0.041     0.081\n",
      "tau[351]            -1.295   0.044    -1.360    -1.328    -1.304    -1.266\n",
      "tau[352]             0.694   0.044     0.627     0.660     0.684     0.724\n",
      "tau[353]             0.519   0.044     0.452     0.486     0.510     0.551\n",
      "tau[354]             0.184   0.044     0.115     0.150     0.175     0.216\n",
      "tau[355]            -0.729   0.044    -0.795    -0.763    -0.739    -0.697\n",
      "tau[356]             0.568   0.043     0.502     0.536     0.559     0.600\n",
      "tau[357]            -0.618   0.044    -0.685    -0.651    -0.629    -0.590\n",
      "tau[358]            -0.504   0.044    -0.569    -0.537    -0.513    -0.475\n",
      "tau[359]             0.703   0.045     0.636     0.670     0.692     0.733\n",
      "tau[360]            -0.444   0.044    -0.511    -0.478    -0.453    -0.414\n",
      "tau[361]            -1.042   0.044    -1.107    -1.075    -1.051    -1.011\n",
      "tau[362]            -0.269   0.044    -0.338    -0.303    -0.279    -0.237\n",
      "tau[363]             0.616   0.044     0.552     0.583     0.607     0.649\n",
      "tau[364]             2.258   0.043     2.194     2.226     2.248     2.290\n",
      "tau[365]             0.129   0.044     0.062     0.096     0.120     0.160\n",
      "tau[366]             0.573   0.043     0.507     0.541     0.564     0.603\n",
      "tau[367]             0.185   0.043     0.118     0.152     0.175     0.216\n",
      "tau[368]             1.063   0.043     0.997     1.029     1.054     1.093\n",
      "tau[369]            -0.048   0.044    -0.116    -0.080    -0.057    -0.014\n",
      "tau[370]             1.629   0.044     1.562     1.596     1.618     1.659\n",
      "tau[371]            -1.642   0.044    -1.709    -1.675    -1.650    -1.610\n",
      "tau[372]            -0.326   0.043    -0.392    -0.360    -0.335    -0.296\n",
      "tau[373]            -1.383   0.044    -1.451    -1.418    -1.392    -1.352\n",
      "tau[374]            -0.418   0.045    -0.486    -0.451    -0.427    -0.387\n",
      "tau[375]            -2.228   0.044    -2.295    -2.262    -2.238    -2.196\n",
      "tau[376]             0.143   0.044     0.077     0.109     0.133     0.172\n",
      "tau[377]             0.128   0.044     0.063     0.095     0.118     0.158\n",
      "tau[378]            -1.226   0.044    -1.296    -1.259    -1.235    -1.195\n",
      "tau[379]            -2.748   0.044    -2.814    -2.781    -2.759    -2.716\n",
      "tau[380]            -1.751   0.044    -1.817    -1.784    -1.760    -1.720\n",
      "tau[381]             1.951   0.044     1.884     1.917     1.941     1.983\n",
      "tau[382]            -0.667   0.043    -0.732    -0.699    -0.676    -0.637\n",
      "tau[383]             0.663   0.043     0.597     0.631     0.653     0.693\n",
      "tau[384]            -1.184   0.044    -1.251    -1.218    -1.195    -1.153\n",
      "tau[385]             0.843   0.044     0.776     0.810     0.834     0.875\n",
      "tau[386]            -0.021   0.044    -0.087    -0.055    -0.031     0.011\n",
      "tau[387]             1.465   0.044     1.400     1.432     1.454     1.496\n",
      "tau[388]             0.124   0.044     0.055     0.091     0.115     0.154\n",
      "tau[389]            -0.553   0.044    -0.619    -0.586    -0.562    -0.520\n",
      "tau[390]            -1.517   0.044    -1.582    -1.550    -1.527    -1.486\n",
      "tau[391]            -1.086   0.045    -1.153    -1.120    -1.095    -1.054\n",
      "tau[392]             1.264   0.044     1.197     1.231     1.255     1.297\n",
      "tau[393]            -1.504   0.044    -1.572    -1.538    -1.514    -1.474\n",
      "tau[394]             0.035   0.044    -0.035     0.003     0.026     0.065\n",
      "tau[395]            -1.057   0.044    -1.124    -1.090    -1.066    -1.026\n",
      "tau[396]            -0.913   0.043    -0.978    -0.946    -0.923    -0.882\n",
      "tau[397]             1.848   0.044     1.781     1.815     1.838     1.880\n",
      "tau[398]            -0.735   0.044    -0.803    -0.769    -0.743    -0.702\n",
      "tau[399]            -0.283   0.044    -0.351    -0.316    -0.292    -0.250\n",
      "tau[400]            -0.931   0.043    -0.998    -0.964    -0.941    -0.901\n",
      "tau[401]            -0.291   0.043    -0.359    -0.323    -0.299    -0.261\n",
      "tau[402]            -0.533   0.043    -0.598    -0.565    -0.542    -0.503\n",
      "tau[403]             1.554   0.044     1.486     1.521     1.546     1.587\n",
      "tau[404]            -0.592   0.044    -0.660    -0.625    -0.602    -0.560\n",
      "tau[405]             0.769   0.044     0.704     0.736     0.759     0.800\n",
      "tau[406]            -0.263   0.044    -0.329    -0.296    -0.273    -0.231\n",
      "tau[407]             1.967   0.044     1.901     1.934     1.957     1.999\n",
      "tau[408]            -0.099   0.043    -0.166    -0.132    -0.107    -0.066\n",
      "tau[409]             0.292   0.043     0.228     0.260     0.283     0.323\n",
      "tau[410]            -1.893   0.043    -1.959    -1.926    -1.903    -1.861\n",
      "tau[411]             0.813   0.044     0.746     0.779     0.803     0.844\n",
      "tau[412]            -0.253   0.045    -0.321    -0.288    -0.263    -0.221\n",
      "tau[413]            -1.699   0.044    -1.767    -1.732    -1.708    -1.670\n",
      "tau[414]             2.130   0.044     2.062     2.096     2.120     2.164\n",
      "tau[415]            -1.684   0.044    -1.750    -1.717    -1.694    -1.652\n",
      "tau[416]            -0.311   0.044    -0.378    -0.344    -0.320    -0.281\n",
      "tau[417]            -0.893   0.044    -0.960    -0.927    -0.903    -0.862\n",
      "tau[418]            -0.148   0.043    -0.212    -0.180    -0.157    -0.118\n",
      "tau[419]             0.713   0.044     0.645     0.679     0.703     0.744\n",
      "tau[420]             0.287   0.043     0.221     0.254     0.279     0.318\n",
      "tau[421]             0.594   0.043     0.526     0.562     0.585     0.623\n",
      "tau[422]             0.506   0.044     0.438     0.473     0.497     0.536\n",
      "tau[423]            -2.545   0.044    -2.611    -2.578    -2.555    -2.515\n",
      "tau[424]            -1.846   0.044    -1.914    -1.880    -1.856    -1.815\n",
      "tau[425]             0.271   0.044     0.205     0.237     0.261     0.303\n",
      "tau[426]             0.651   0.044     0.583     0.618     0.642     0.679\n",
      "tau[427]             0.321   0.044     0.251     0.286     0.311     0.354\n",
      "tau[428]            -0.175   0.044    -0.242    -0.207    -0.184    -0.142\n",
      "tau[429]            -0.746   0.044    -0.812    -0.779    -0.756    -0.713\n",
      "tau[430]            -0.075   0.044    -0.143    -0.109    -0.085    -0.045\n",
      "tau[431]             2.961   0.044     2.894     2.928     2.951     2.991\n",
      "tau[432]            -0.353   0.044    -0.419    -0.386    -0.363    -0.323\n",
      "tau[433]            -0.121   0.044    -0.190    -0.154    -0.130    -0.090\n",
      "tau[434]            -0.384   0.044    -0.449    -0.417    -0.394    -0.353\n",
      "tau[435]             0.224   0.044     0.157     0.191     0.214     0.255\n",
      "tau[436]            -2.087   0.044    -2.154    -2.121    -2.097    -2.055\n",
      "tau[437]            -1.156   0.044    -1.223    -1.190    -1.165    -1.125\n",
      "tau[438]            -2.488   0.044    -2.556    -2.521    -2.498    -2.458\n",
      "tau[439]             0.225   0.044     0.158     0.191     0.215     0.256\n",
      "tau[440]            -0.929   0.044    -0.998    -0.962    -0.938    -0.899\n",
      "tau[441]             0.888   0.044     0.818     0.855     0.877     0.920\n",
      "tau[442]             0.517   0.044     0.450     0.483     0.508     0.547\n",
      "tau[443]             0.374   0.043     0.309     0.340     0.365     0.405\n",
      "tau[444]            -0.853   0.044    -0.921    -0.886    -0.863    -0.820\n",
      "tau[445]             0.359   0.043     0.292     0.327     0.350     0.390\n",
      "tau[446]            -0.989   0.043    -1.054    -1.021    -0.999    -0.959\n",
      "tau[447]            -0.476   0.044    -0.543    -0.509    -0.486    -0.446\n",
      "tau[448]            -1.001   0.043    -1.067    -1.034    -1.011    -0.970\n",
      "tau[449]            -0.251   0.044    -0.317    -0.284    -0.261    -0.218\n",
      "tau[450]            -0.019   0.044    -0.084    -0.052    -0.030     0.012\n",
      "tau[451]            -1.200   0.043    -1.269    -1.234    -1.208    -1.169\n",
      "tau[452]             0.564   0.043     0.499     0.532     0.554     0.596\n",
      "tau[453]             0.145   0.044     0.079     0.112     0.135     0.175\n",
      "tau[454]            -0.258   0.043    -0.324    -0.291    -0.267    -0.228\n",
      "tau[455]             0.527   0.044     0.460     0.494     0.517     0.558\n",
      "tau[456]            -0.466   0.044    -0.534    -0.499    -0.474    -0.435\n",
      "tau[457]             0.974   0.044     0.908     0.940     0.964     1.006\n",
      "tau[458]             0.709   0.044     0.642     0.677     0.700     0.739\n",
      "tau[459]            -0.821   0.043    -0.888    -0.854    -0.830    -0.791\n",
      "tau[460]            -1.201   0.044    -1.268    -1.233    -1.210    -1.170\n",
      "tau[461]            -0.579   0.044    -0.645    -0.612    -0.589    -0.546\n",
      "tau[462]            -0.505   0.044    -0.573    -0.537    -0.515    -0.472\n",
      "tau[463]             1.252   0.044     1.185     1.219     1.243     1.284\n",
      "tau[464]             1.604   0.043     1.537     1.571     1.595     1.635\n",
      "tau[465]             0.852   0.044     0.788     0.818     0.843     0.880\n",
      "tau[466]            -0.108   0.045    -0.177    -0.142    -0.117    -0.075\n",
      "tau[467]             0.794   0.043     0.729     0.761     0.786     0.825\n",
      "tau[468]             1.381   0.044     1.313     1.348     1.372     1.413\n",
      "tau[469]             1.076   0.044     1.008     1.042     1.066     1.106\n",
      "tau[470]             1.400   0.043     1.332     1.368     1.391     1.431\n",
      "tau[471]            -0.180   0.045    -0.248    -0.214    -0.190    -0.149\n",
      "tau[472]            -0.960   0.044    -1.027    -0.994    -0.969    -0.929\n",
      "tau[473]             2.120   0.044     2.051     2.087     2.110     2.152\n",
      "tau[474]            -0.493   0.044    -0.558    -0.526    -0.502    -0.461\n",
      "tau[475]             1.251   0.043     1.184     1.218     1.242     1.282\n",
      "tau[476]            -0.442   0.043    -0.511    -0.474    -0.452    -0.411\n",
      "tau[477]             2.310   0.044     2.244     2.276     2.301     2.342\n",
      "tau[478]             0.911   0.044     0.846     0.877     0.902     0.942\n",
      "tau[479]             0.774   0.043     0.707     0.742     0.764     0.806\n",
      "tau[480]             0.359   0.044     0.290     0.325     0.350     0.391\n",
      "tau[481]             0.991   0.044     0.926     0.958     0.981     1.022\n",
      "tau[482]            -0.585   0.044    -0.650    -0.619    -0.595    -0.555\n",
      "tau[483]            -0.349   0.044    -0.413    -0.382    -0.360    -0.317\n",
      "tau[484]            -0.745   0.044    -0.813    -0.779    -0.755    -0.714\n",
      "tau[485]            -0.004   0.044    -0.072    -0.037    -0.014     0.027\n",
      "tau[486]             0.359   0.044     0.292     0.325     0.349     0.392\n",
      "tau[487]             1.098   0.044     1.028     1.065     1.090     1.128\n",
      "tau[488]            -1.179   0.044    -1.246    -1.211    -1.189    -1.148\n",
      "tau[489]            -0.398   0.044    -0.466    -0.432    -0.408    -0.364\n",
      "tau[490]            -1.144   0.044    -1.213    -1.177    -1.153    -1.115\n",
      "tau[491]             0.609   0.044     0.539     0.576     0.599     0.641\n",
      "tau[492]             0.611   0.044     0.544     0.578     0.602     0.642\n",
      "tau[493]             1.421   0.044     1.353     1.389     1.411     1.452\n",
      "tau[494]             0.386   0.044     0.319     0.353     0.375     0.416\n",
      "tau[495]             0.467   0.044     0.400     0.433     0.458     0.499\n",
      "tau[496]            -1.272   0.044    -1.344    -1.305    -1.282    -1.241\n",
      "tau[497]            -0.644   0.044    -0.711    -0.677    -0.653    -0.612\n",
      "tau[498]             0.392   0.044     0.324     0.359     0.382     0.423\n",
      "tau[499]             1.798   0.043     1.732     1.766     1.787     1.827\n",
      "tau[500]             0.114   0.044     0.046     0.081     0.104     0.146\n",
      "time_discrm[1]      11.951   0.397    11.148    11.680    11.937    12.216\n",
      "time_discrm[2]      11.013   0.350    10.338    10.785    11.016    11.247\n",
      "time_discrm[3]      11.875   0.400    11.115    11.609    11.867    12.145\n",
      "time_discrm[4]      11.925   0.400    11.125    11.661    11.931    12.181\n",
      "time_discrm[5]      10.984   0.344    10.318    10.749    10.980    11.222\n",
      "time_discrm[6]      12.197   0.410    11.412    11.918    12.196    12.464\n",
      "time_discrm[7]      11.194   0.363    10.479    10.961    11.187    11.443\n",
      "time_discrm[8]      11.027   0.366    10.309    10.783    11.031    11.261\n",
      "time_discrm[9]      11.219   0.379    10.488    10.970    11.204    11.469\n",
      "time_discrm[10]     11.513   0.378    10.797    11.245    11.504    11.781\n",
      "time_discrm[11]     11.720   0.386    10.991    11.450    11.708    11.971\n",
      "time_discrm[12]     11.592   0.404    10.793    11.319    11.594    11.859\n",
      "time_discrm[13]     12.211   0.419    11.401    11.927    12.215    12.501\n",
      "time_discrm[14]     10.996   0.379    10.265    10.732    10.993    11.253\n",
      "time_discrm[15]     11.360   0.372    10.639    11.106    11.354    11.617\n",
      "time_discrm[16]     11.959   0.393    11.221    11.697    11.945    12.226\n",
      "time_discrm[17]     11.452   0.376    10.761    11.188    11.444    11.701\n",
      "time_discrm[18]     11.857   0.393    11.123    11.589    11.860    12.128\n",
      "time_discrm[19]     11.647   0.388    10.900    11.385    11.659    11.904\n",
      "time_discrm[20]     11.579   0.387    10.871    11.302    11.575    11.842\n",
      "time_intense[1]      3.384   0.040     3.335     3.353     3.371     3.414\n",
      "time_intense[2]      2.656   0.039     2.607     2.625     2.643     2.686\n",
      "time_intense[3]      2.911   0.039     2.862     2.879     2.897     2.940\n",
      "time_intense[4]      4.183   0.039     4.135     4.152     4.170     4.213\n",
      "time_intense[5]      1.669   0.040     1.620     1.638     1.656     1.699\n",
      "time_intense[6]      1.223   0.040     1.174     1.191     1.210     1.253\n",
      "time_intense[7]      4.114   0.040     4.065     4.082     4.100     4.143\n",
      "time_intense[8]      3.900   0.039     3.851     3.869     3.886     3.929\n",
      "time_intense[9]      3.362   0.039     3.313     3.330     3.348     3.391\n",
      "time_intense[10]     2.452   0.039     2.403     2.420     2.438     2.481\n",
      "time_intense[11]     3.851   0.040     3.802     3.820     3.837     3.881\n",
      "time_intense[12]     2.569   0.039     2.521     2.537     2.555     2.598\n",
      "time_intense[13]     2.059   0.039     2.010     2.027     2.045     2.088\n",
      "time_intense[14]     2.705   0.039     2.657     2.674     2.692     2.736\n",
      "time_intense[15]     3.645   0.040     3.596     3.614     3.632     3.675\n",
      "time_intense[16]     1.638   0.039     1.590     1.607     1.625     1.667\n",
      "time_intense[17]     4.040   0.039     3.992     4.008     4.027     4.070\n",
      "time_intense[18]     3.551   0.040     3.502     3.519     3.538     3.581\n",
      "time_intense[19]     2.875   0.039     2.826     2.843     2.862     2.905\n",
      "time_intense[20]     4.664   0.040     4.615     4.632     4.650     4.694\n",
      "deviance         42138.319  33.651 42071.558 42115.394 42137.970 42160.391\n",
      "                     97.5%  Rhat n.eff\n",
      "tau[1]               0.504 2.727     3\n",
      "tau[2]               0.944 2.610     3\n",
      "tau[3]              -0.458 2.579     3\n",
      "tau[4]               0.224 2.621     3\n",
      "tau[5]               0.208 2.551     3\n",
      "tau[6]              -1.047 2.644     3\n",
      "tau[7]               0.061 2.622     3\n",
      "tau[8]               1.347 2.705     3\n",
      "tau[9]              -0.001 2.641     3\n",
      "tau[10]             -0.576 2.642     3\n",
      "tau[11]             -0.301 2.574     3\n",
      "tau[12]             -0.466 2.581     3\n",
      "tau[13]             -0.215 2.589     3\n",
      "tau[14]              0.445 2.645     3\n",
      "tau[15]             -0.266 2.612     3\n",
      "tau[16]             -0.064 2.629     3\n",
      "tau[17]             -0.553 2.650     3\n",
      "tau[18]              0.485 2.567     3\n",
      "tau[19]              0.495 2.650     3\n",
      "tau[20]             -0.969 2.630     3\n",
      "tau[21]             -0.049 2.554     3\n",
      "tau[22]             -0.424 2.638     3\n",
      "tau[23]              1.259 2.676     3\n",
      "tau[24]              1.039 2.687     3\n",
      "tau[25]             -1.709 2.695     3\n",
      "tau[26]             -1.865 2.563     3\n",
      "tau[27]              0.866 2.615     3\n",
      "tau[28]              0.903 2.677     3\n",
      "tau[29]              0.034 2.671     3\n",
      "tau[30]             -0.772 2.758     3\n",
      "tau[31]              0.545 2.631     3\n",
      "tau[32]             -0.949 2.606     3\n",
      "tau[33]              0.675 2.572     3\n",
      "tau[34]              0.749 2.785     3\n",
      "tau[35]             -0.558 2.647     3\n",
      "tau[36]             -0.377 2.608     3\n",
      "tau[37]              0.183 2.593     3\n",
      "tau[38]             -0.055 2.622     3\n",
      "tau[39]              0.179 2.616     3\n",
      "tau[40]              0.922 2.637     3\n",
      "tau[41]              0.747 2.630     3\n",
      "tau[42]             -0.808 2.582     3\n",
      "tau[43]             -0.718 2.594     3\n",
      "tau[44]              1.446 2.684     3\n",
      "tau[45]              0.867 2.668     3\n",
      "tau[46]             -0.470 2.595     3\n",
      "tau[47]              0.217 2.561     3\n",
      "tau[48]             -0.876 2.731     3\n",
      "tau[49]             -0.588 2.570     3\n",
      "tau[50]              0.167 2.602     3\n",
      "tau[51]             -1.745 2.740     3\n",
      "tau[52]             -1.183 2.563     3\n",
      "tau[53]             -0.956 2.587     3\n",
      "tau[54]             -2.348 2.590     3\n",
      "tau[55]              1.061 2.685     3\n",
      "tau[56]              0.402 2.663     3\n",
      "tau[57]             -0.229 2.649     3\n",
      "tau[58]             -0.054 2.679     3\n",
      "tau[59]             -0.371 2.631     3\n",
      "tau[60]              1.096 2.638     3\n",
      "tau[61]             -0.741 2.533     3\n",
      "tau[62]             -1.123 2.675     3\n",
      "tau[63]              0.440 2.686     3\n",
      "tau[64]             -0.006 2.593     3\n",
      "tau[65]              0.731 2.721     3\n",
      "tau[66]              0.372 2.629     3\n",
      "tau[67]             -0.732 2.715     3\n",
      "tau[68]             -0.413 2.549     3\n",
      "tau[69]              0.116 2.697     3\n",
      "tau[70]             -0.802 2.698     3\n",
      "tau[71]             -1.130 2.600     3\n",
      "tau[72]              0.801 2.744     3\n",
      "tau[73]              1.486 2.681     3\n",
      "tau[74]              0.466 2.680     3\n",
      "tau[75]             -1.287 2.503     3\n",
      "tau[76]             -0.743 2.702     3\n",
      "tau[77]              0.060 2.675     3\n",
      "tau[78]              1.393 2.679     3\n",
      "tau[79]              0.453 2.721     3\n",
      "tau[80]              0.250 2.668     3\n",
      "tau[81]             -1.541 2.705     3\n",
      "tau[82]             -1.121 2.660     3\n",
      "tau[83]             -0.584 2.592     3\n",
      "tau[84]              0.889 2.663     3\n",
      "tau[85]             -0.391 2.690     3\n",
      "tau[86]             -0.842 2.750     3\n",
      "tau[87]             -2.779 2.690     3\n",
      "tau[88]             -0.003 2.633     3\n",
      "tau[89]             -0.213 2.662     3\n",
      "tau[90]             -0.500 2.733     3\n",
      "tau[91]              0.309 2.664     3\n",
      "tau[92]             -0.730 2.663     3\n",
      "tau[93]             -0.075 2.556     3\n",
      "tau[94]              0.001 2.730     3\n",
      "tau[95]              0.213 2.567     3\n",
      "tau[96]             -0.489 2.755     3\n",
      "tau[97]              0.647 2.672     3\n",
      "tau[98]             -0.896 2.579     3\n",
      "tau[99]              1.110 2.626     3\n",
      "tau[100]            -1.689 2.694     3\n",
      "tau[101]            -0.388 2.638     3\n",
      "tau[102]            -0.802 2.582     3\n",
      "tau[103]             0.525 2.699     3\n",
      "tau[104]             0.715 2.691     3\n",
      "tau[105]            -0.397 2.581     3\n",
      "tau[106]             0.424 2.602     3\n",
      "tau[107]             1.164 2.646     3\n",
      "tau[108]            -0.350 2.626     3\n",
      "tau[109]             2.364 2.595     3\n",
      "tau[110]             1.559 2.549     3\n",
      "tau[111]            -1.034 2.599     3\n",
      "tau[112]             0.969 2.586     3\n",
      "tau[113]             0.153 2.627     3\n",
      "tau[114]            -0.032 2.678     3\n",
      "tau[115]             0.148 2.673     3\n",
      "tau[116]             1.415 2.806     3\n",
      "tau[117]             0.471 2.632     3\n",
      "tau[118]             0.458 2.718     3\n",
      "tau[119]             0.657 2.597     3\n",
      "tau[120]            -0.061 2.586     3\n",
      "tau[121]             0.457 2.617     3\n",
      "tau[122]             0.096 2.559     3\n",
      "tau[123]             1.467 2.616     3\n",
      "tau[124]            -1.934 2.688     3\n",
      "tau[125]             0.904 2.650     3\n",
      "tau[126]             0.042 2.601     3\n",
      "tau[127]             1.146 2.682     3\n",
      "tau[128]             0.118 2.585     3\n",
      "tau[129]            -0.140 2.692     3\n",
      "tau[130]            -1.702 2.585     3\n",
      "tau[131]            -0.838 2.626     3\n",
      "tau[132]            -0.079 2.632     3\n",
      "tau[133]             0.338 2.670     3\n",
      "tau[134]            -0.862 2.682     3\n",
      "tau[135]            -0.885 2.695     3\n",
      "tau[136]            -1.250 2.585     3\n",
      "tau[137]             0.463 2.695     3\n",
      "tau[138]            -1.430 2.646     3\n",
      "tau[139]            -0.466 2.646     3\n",
      "tau[140]             0.120 2.547     3\n",
      "tau[141]            -0.994 2.697     3\n",
      "tau[142]             0.997 2.630     3\n",
      "tau[143]            -1.863 2.678     3\n",
      "tau[144]             0.103 2.680     3\n",
      "tau[145]            -1.754 2.589     3\n",
      "tau[146]             1.892 2.565     3\n",
      "tau[147]            -0.245 2.695     3\n",
      "tau[148]            -0.334 2.649     3\n",
      "tau[149]             0.065 2.616     3\n",
      "tau[150]             0.287 2.604     3\n",
      "tau[151]             0.582 2.628     3\n",
      "tau[152]             0.578 2.613     3\n",
      "tau[153]            -0.240 2.598     3\n",
      "tau[154]            -0.283 2.639     3\n",
      "tau[155]            -1.734 2.625     3\n",
      "tau[156]            -0.995 2.662     3\n",
      "tau[157]             0.376 2.708     3\n",
      "tau[158]             0.974 2.677     3\n",
      "tau[159]             0.639 2.680     3\n",
      "tau[160]             0.148 2.616     3\n",
      "tau[161]            -0.961 2.627     3\n",
      "tau[162]             0.414 2.571     3\n",
      "tau[163]             0.423 2.650     3\n",
      "tau[164]             0.337 2.642     3\n",
      "tau[165]            -0.123 2.624     3\n",
      "tau[166]            -1.269 2.664     3\n",
      "tau[167]             0.162 2.630     3\n",
      "tau[168]             0.566 2.719     3\n",
      "tau[169]             0.130 2.616     3\n",
      "tau[170]            -0.768 2.592     3\n",
      "tau[171]            -0.443 2.553     3\n",
      "tau[172]            -0.160 2.624     3\n",
      "tau[173]            -0.636 2.589     3\n",
      "tau[174]             0.410 2.706     3\n",
      "tau[175]            -0.213 2.713     3\n",
      "tau[176]            -0.063 2.616     3\n",
      "tau[177]             0.371 2.661     3\n",
      "tau[178]            -1.565 2.662     3\n",
      "tau[179]            -0.824 2.651     3\n",
      "tau[180]            -1.263 2.636     3\n",
      "tau[181]            -0.392 2.618     3\n",
      "tau[182]            -0.892 2.605     3\n",
      "tau[183]             1.208 2.631     3\n",
      "tau[184]             0.586 2.656     3\n",
      "tau[185]             2.064 2.599     3\n",
      "tau[186]             0.239 2.685     3\n",
      "tau[187]             0.190 2.540     3\n",
      "tau[188]             1.857 2.692     3\n",
      "tau[189]             0.521 2.764     3\n",
      "tau[190]             1.498 2.718     3\n",
      "tau[191]             1.329 2.675     3\n",
      "tau[192]             0.567 2.687     3\n",
      "tau[193]            -0.208 2.598     3\n",
      "tau[194]            -0.889 2.701     3\n",
      "tau[195]             1.196 2.692     3\n",
      "tau[196]            -0.230 2.651     3\n",
      "tau[197]            -0.608 2.706     3\n",
      "tau[198]             0.543 2.675     3\n",
      "tau[199]             0.757 2.583     3\n",
      "tau[200]            -0.671 2.722     3\n",
      "tau[201]            -1.150 2.633     3\n",
      "tau[202]             0.466 2.609     3\n",
      "tau[203]             0.543 2.645     3\n",
      "tau[204]            -0.780 2.687     3\n",
      "tau[205]            -0.524 2.622     3\n",
      "tau[206]            -1.358 2.625     3\n",
      "tau[207]            -0.592 2.596     3\n",
      "tau[208]             0.092 2.583     3\n",
      "tau[209]            -1.407 2.746     3\n",
      "tau[210]            -0.836 2.606     3\n",
      "tau[211]            -0.375 2.643     3\n",
      "tau[212]            -2.267 2.624     3\n",
      "tau[213]             0.811 2.616     3\n",
      "tau[214]             0.686 2.698     3\n",
      "tau[215]             0.031 2.675     3\n",
      "tau[216]             0.590 2.708     3\n",
      "tau[217]            -0.534 2.668     3\n",
      "tau[218]            -1.477 2.594     3\n",
      "tau[219]             0.188 2.675     3\n",
      "tau[220]             1.349 2.613     3\n",
      "tau[221]             1.869 2.561     3\n",
      "tau[222]             2.887 2.640     3\n",
      "tau[223]             0.966 2.639     3\n",
      "tau[224]            -0.633 2.670     3\n",
      "tau[225]             0.557 2.655     3\n",
      "tau[226]             0.386 2.696     3\n",
      "tau[227]            -0.313 2.607     3\n",
      "tau[228]             1.494 2.669     3\n",
      "tau[229]            -0.526 2.674     3\n",
      "tau[230]            -0.031 2.637     3\n",
      "tau[231]            -0.302 2.583     3\n",
      "tau[232]            -1.257 2.694     3\n",
      "tau[233]            -0.972 2.638     3\n",
      "tau[234]             1.111 2.745     3\n",
      "tau[235]             0.030 2.583     3\n",
      "tau[236]            -1.366 2.557     3\n",
      "tau[237]            -0.514 2.609     3\n",
      "tau[238]             0.847 2.673     3\n",
      "tau[239]             0.891 2.583     3\n",
      "tau[240]             1.175 2.638     3\n",
      "tau[241]             1.398 2.453     3\n",
      "tau[242]             0.379 2.740     3\n",
      "tau[243]             1.595 2.574     3\n",
      "tau[244]             0.829 2.642     3\n",
      "tau[245]             0.006 2.627     3\n",
      "tau[246]            -0.463 2.604     3\n",
      "tau[247]            -0.887 2.622     3\n",
      "tau[248]             2.438 2.669     3\n",
      "tau[249]             0.756 2.667     3\n",
      "tau[250]             0.314 2.823     3\n",
      "tau[251]             2.065 2.575     3\n",
      "tau[252]             0.248 2.673     3\n",
      "tau[253]             0.100 2.640     3\n",
      "tau[254]            -0.968 2.686     3\n",
      "tau[255]            -0.720 2.617     3\n",
      "tau[256]             0.114 2.604     3\n",
      "tau[257]            -1.209 2.652     3\n",
      "tau[258]             0.032 2.622     3\n",
      "tau[259]             0.138 2.581     3\n",
      "tau[260]            -2.874 2.598     3\n",
      "tau[261]            -1.720 2.609     3\n",
      "tau[262]            -0.145 2.673     3\n",
      "tau[263]             0.072 2.560     3\n",
      "tau[264]             2.869 2.600     3\n",
      "tau[265]             0.191 2.472     3\n",
      "tau[266]            -0.941 2.678     3\n",
      "tau[267]             0.287 2.700     3\n",
      "tau[268]             0.361 2.659     3\n",
      "tau[269]             3.271 2.622     3\n",
      "tau[270]             0.208 2.546     3\n",
      "tau[271]             0.930 2.685     3\n",
      "tau[272]             0.172 2.689     3\n",
      "tau[273]            -0.122 2.651     3\n",
      "tau[274]             0.106 2.592     3\n",
      "tau[275]            -0.739 2.627     3\n",
      "tau[276]            -1.451 2.607     3\n",
      "tau[277]            -0.580 2.744     3\n",
      "tau[278]            -1.020 2.655     3\n",
      "tau[279]             1.138 2.653     3\n",
      "tau[280]            -0.872 2.630     3\n",
      "tau[281]             0.349 2.657     3\n",
      "tau[282]            -0.410 2.604     3\n",
      "tau[283]             0.145 2.655     3\n",
      "tau[284]            -0.286 2.508     3\n",
      "tau[285]             0.555 2.565     3\n",
      "tau[286]            -1.098 2.672     3\n",
      "tau[287]             0.834 2.695     3\n",
      "tau[288]             1.574 2.625     3\n",
      "tau[289]             0.596 2.583     3\n",
      "tau[290]             1.039 2.621     3\n",
      "tau[291]             1.094 2.735     3\n",
      "tau[292]            -0.359 2.589     3\n",
      "tau[293]             1.920 2.657     3\n",
      "tau[294]             0.685 2.594     3\n",
      "tau[295]             1.539 2.590     3\n",
      "tau[296]            -0.009 2.671     3\n",
      "tau[297]             0.416 2.633     3\n",
      "tau[298]            -0.967 2.645     3\n",
      "tau[299]             0.659 2.673     3\n",
      "tau[300]             0.243 2.634     3\n",
      "tau[301]             0.909 2.766     3\n",
      "tau[302]            -0.865 2.629     3\n",
      "tau[303]            -1.275 2.584     3\n",
      "tau[304]             1.300 2.664     3\n",
      "tau[305]            -1.334 2.629     3\n",
      "tau[306]             0.279 2.701     3\n",
      "tau[307]            -0.117 2.653     3\n",
      "tau[308]            -2.657 2.611     3\n",
      "tau[309]            -0.660 2.677     3\n",
      "tau[310]             0.547 2.616     3\n",
      "tau[311]            -0.040 2.689     3\n",
      "tau[312]            -0.331 2.570     3\n",
      "tau[313]             0.387 2.666     3\n",
      "tau[314]            -0.160 2.690     3\n",
      "tau[315]             1.159 2.648     3\n",
      "tau[316]            -0.576 2.596     3\n",
      "tau[317]             2.647 2.589     3\n",
      "tau[318]            -1.371 2.610     3\n",
      "tau[319]            -0.115 2.592     3\n",
      "tau[320]            -2.015 2.616     3\n",
      "tau[321]             0.378 2.609     3\n",
      "tau[322]             0.766 2.659     3\n",
      "tau[323]             0.264 2.699     3\n",
      "tau[324]             0.586 2.719     3\n",
      "tau[325]             0.374 2.589     3\n",
      "tau[326]             0.130 2.702     3\n",
      "tau[327]             0.704 2.757     3\n",
      "tau[328]            -0.954 2.565     3\n",
      "tau[329]             0.104 2.647     3\n",
      "tau[330]            -0.451 2.639     3\n",
      "tau[331]             1.200 2.601     3\n",
      "tau[332]             2.412 2.636     3\n",
      "tau[333]             1.309 2.503     3\n",
      "tau[334]             0.082 2.639     3\n",
      "tau[335]             0.919 2.644     3\n",
      "tau[336]            -0.541 2.614     3\n",
      "tau[337]            -0.332 2.636     3\n",
      "tau[338]            -0.854 2.620     3\n",
      "tau[339]             0.684 2.516     3\n",
      "tau[340]             1.519 2.675     3\n",
      "tau[341]            -0.023 2.619     3\n",
      "tau[342]            -0.805 2.629     3\n",
      "tau[343]            -2.266 2.614     3\n",
      "tau[344]             0.405 2.504     3\n",
      "tau[345]            -0.545 2.702     3\n",
      "tau[346]             0.223 2.691     3\n",
      "tau[347]             0.799 2.590     3\n",
      "tau[348]            -1.745 2.624     3\n",
      "tau[349]            -0.803 2.661     3\n",
      "tau[350]             0.140 2.633     3\n",
      "tau[351]            -1.202 2.573     3\n",
      "tau[352]             0.787 2.724     3\n",
      "tau[353]             0.611 2.649     3\n",
      "tau[354]             0.275 2.700     3\n",
      "tau[355]            -0.640 2.583     3\n",
      "tau[356]             0.659 2.638     3\n",
      "tau[357]            -0.525 2.624     3\n",
      "tau[358]            -0.412 2.631     3\n",
      "tau[359]             0.795 2.637     3\n",
      "tau[360]            -0.355 2.672     3\n",
      "tau[361]            -0.948 2.603     3\n",
      "tau[362]            -0.181 2.668     3\n",
      "tau[363]             0.706 2.675     3\n",
      "tau[364]             2.349 2.669     3\n",
      "tau[365]             0.221 2.562     3\n",
      "tau[366]             0.663 2.632     3\n",
      "tau[367]             0.275 2.725     3\n",
      "tau[368]             1.151 2.695     3\n",
      "tau[369]             0.044 2.630     3\n",
      "tau[370]             1.720 2.644     3\n",
      "tau[371]            -1.552 2.693     3\n",
      "tau[372]            -0.237 2.648     3\n",
      "tau[373]            -1.291 2.642     3\n",
      "tau[374]            -0.324 2.669     3\n",
      "tau[375]            -2.134 2.593     3\n",
      "tau[376]             0.233 2.616     3\n",
      "tau[377]             0.220 2.707     3\n",
      "tau[378]            -1.137 2.631     3\n",
      "tau[379]            -2.656 2.663     3\n",
      "tau[380]            -1.662 2.689     3\n",
      "tau[381]             2.043 2.598     3\n",
      "tau[382]            -0.576 2.623     3\n",
      "tau[383]             0.752 2.666     3\n",
      "tau[384]            -1.093 2.559     3\n",
      "tau[385]             0.934 2.664     3\n",
      "tau[386]             0.070 2.674     3\n",
      "tau[387]             1.558 2.596     3\n",
      "tau[388]             0.216 2.506     3\n",
      "tau[389]            -0.461 2.674     3\n",
      "tau[390]            -1.426 2.669     3\n",
      "tau[391]            -0.995 2.635     3\n",
      "tau[392]             1.355 2.718     3\n",
      "tau[393]            -1.413 2.600     3\n",
      "tau[394]             0.126 2.659     3\n",
      "tau[395]            -0.968 2.587     3\n",
      "tau[396]            -0.826 2.635     3\n",
      "tau[397]             1.938 2.605     3\n",
      "tau[398]            -0.645 2.645     3\n",
      "tau[399]            -0.193 2.670     3\n",
      "tau[400]            -0.842 2.626     3\n",
      "tau[401]            -0.200 2.623     3\n",
      "tau[402]            -0.445 2.630     3\n",
      "tau[403]             1.643 2.761     3\n",
      "tau[404]            -0.501 2.666     3\n",
      "tau[405]             0.861 2.679     3\n",
      "tau[406]            -0.171 2.631     3\n",
      "tau[407]             2.059 2.627     3\n",
      "tau[408]            -0.010 2.588     3\n",
      "tau[409]             0.379 2.660     3\n",
      "tau[410]            -1.806 2.647     3\n",
      "tau[411]             0.906 2.660     3\n",
      "tau[412]            -0.163 2.696     3\n",
      "tau[413]            -1.610 2.640     3\n",
      "tau[414]             2.220 2.648     3\n",
      "tau[415]            -1.593 2.649     3\n",
      "tau[416]            -0.219 2.614     3\n",
      "tau[417]            -0.801 2.674     3\n",
      "tau[418]            -0.058 2.625     3\n",
      "tau[419]             0.805 2.723     3\n",
      "tau[420]             0.377 2.658     3\n",
      "tau[421]             0.685 2.580     3\n",
      "tau[422]             0.596 2.620     3\n",
      "tau[423]            -2.452 2.655     3\n",
      "tau[424]            -1.756 2.663     3\n",
      "tau[425]             0.360 2.680     3\n",
      "tau[426]             0.744 2.561     3\n",
      "tau[427]             0.411 2.714     3\n",
      "tau[428]            -0.084 2.681     3\n",
      "tau[429]            -0.654 2.651     3\n",
      "tau[430]             0.016 2.717     3\n",
      "tau[431]             3.053 2.593     3\n",
      "tau[432]            -0.261 2.539     3\n",
      "tau[433]            -0.031 2.597     3\n",
      "tau[434]            -0.293 2.650     3\n",
      "tau[435]             0.315 2.701     3\n",
      "tau[436]            -1.996 2.719     3\n",
      "tau[437]            -1.063 2.661     3\n",
      "tau[438]            -2.396 2.568     3\n",
      "tau[439]             0.316 2.674     3\n",
      "tau[440]            -0.841 2.646     3\n",
      "tau[441]             0.979 2.646     3\n",
      "tau[442]             0.611 2.657     3\n",
      "tau[443]             0.463 2.645     3\n",
      "tau[444]            -0.762 2.689     3\n",
      "tau[445]             0.448 2.698     3\n",
      "tau[446]            -0.899 2.647     3\n",
      "tau[447]            -0.384 2.599     3\n",
      "tau[448]            -0.913 2.619     3\n",
      "tau[449]            -0.159 2.609     3\n",
      "tau[450]             0.072 2.636     3\n",
      "tau[451]            -1.112 2.660     3\n",
      "tau[452]             0.652 2.709     3\n",
      "tau[453]             0.238 2.663     3\n",
      "tau[454]            -0.167 2.601     3\n",
      "tau[455]             0.621 2.747     3\n",
      "tau[456]            -0.377 2.582     3\n",
      "tau[457]             1.065 2.629     3\n",
      "tau[458]             0.799 2.639     3\n",
      "tau[459]            -0.733 2.745     3\n",
      "tau[460]            -1.109 2.578     3\n",
      "tau[461]            -0.487 2.682     3\n",
      "tau[462]            -0.417 2.651     3\n",
      "tau[463]             1.345 2.650     3\n",
      "tau[464]             1.692 2.656     3\n",
      "tau[465]             0.942 2.625     3\n",
      "tau[466]            -0.017 2.661     3\n",
      "tau[467]             0.883 2.660     3\n",
      "tau[468]             1.471 2.727     3\n",
      "tau[469]             1.168 2.638     3\n",
      "tau[470]             1.488 2.668     3\n",
      "tau[471]            -0.090 2.577     3\n",
      "tau[472]            -0.868 2.632     3\n",
      "tau[473]             2.215 2.612     3\n",
      "tau[474]            -0.401 2.651     3\n",
      "tau[475]             1.340 2.604     3\n",
      "tau[476]            -0.352 2.582     3\n",
      "tau[477]             2.403 2.612     3\n",
      "tau[478]             1.000 2.634     3\n",
      "tau[479]             0.863 2.636     3\n",
      "tau[480]             0.450 2.687     3\n",
      "tau[481]             1.081 2.665     3\n",
      "tau[482]            -0.496 2.610     3\n",
      "tau[483]            -0.259 2.640     3\n",
      "tau[484]            -0.655 2.716     3\n",
      "tau[485]             0.087 2.642     3\n",
      "tau[486]             0.450 2.730     3\n",
      "tau[487]             1.188 2.661     3\n",
      "tau[488]            -1.090 2.691     3\n",
      "tau[489]            -0.308 2.665     3\n",
      "tau[490]            -1.055 2.665     3\n",
      "tau[491]             0.700 2.602     3\n",
      "tau[492]             0.703 2.627     3\n",
      "tau[493]             1.510 2.618     3\n",
      "tau[494]             0.476 2.655     3\n",
      "tau[495]             0.558 2.658     3\n",
      "tau[496]            -1.182 2.623     3\n",
      "tau[497]            -0.554 2.548     3\n",
      "tau[498]             0.482 2.685     3\n",
      "tau[499]             1.887 2.622     3\n",
      "tau[500]             0.206 2.597     3\n",
      "time_discrm[1]      12.745 1.001  2000\n",
      "time_discrm[2]      11.683 1.001  2000\n",
      "time_discrm[3]      12.665 1.001  2000\n",
      "time_discrm[4]      12.680 1.001  2000\n",
      "time_discrm[5]      11.657 1.001  2000\n",
      "time_discrm[6]      12.983 1.001  2000\n",
      "time_discrm[7]      11.902 1.001  2000\n",
      "time_discrm[8]      11.744 1.001  2000\n",
      "time_discrm[9]      11.997 1.003  2000\n",
      "time_discrm[10]     12.251 1.001  2000\n",
      "time_discrm[11]     12.495 1.001  2000\n",
      "time_discrm[12]     12.380 1.001  2000\n",
      "time_discrm[13]     13.006 1.001  2000\n",
      "time_discrm[14]     11.733 1.001  2000\n",
      "time_discrm[15]     12.094 1.002  2000\n",
      "time_discrm[16]     12.777 1.001  2000\n",
      "time_discrm[17]     12.219 1.001  1600\n",
      "time_discrm[18]     12.616 1.001  2000\n",
      "time_discrm[19]     12.393 1.002  2000\n",
      "time_discrm[20]     12.361 1.001  2000\n",
      "time_intense[1]      3.463 3.316     3\n",
      "time_intense[2]      2.736 3.250     3\n",
      "time_intense[3]      2.988 3.294     3\n",
      "time_intense[4]      4.262 3.265     3\n",
      "time_intense[5]      1.748 3.261     3\n",
      "time_intense[6]      1.301 3.317     3\n",
      "time_intense[7]      4.192 3.255     3\n",
      "time_intense[8]      3.978 3.270     3\n",
      "time_intense[9]      3.440 3.258     3\n",
      "time_intense[10]     2.530 3.302     3\n",
      "time_intense[11]     3.929 3.251     3\n",
      "time_intense[12]     2.646 3.275     3\n",
      "time_intense[13]     2.137 3.274     3\n",
      "time_intense[14]     2.783 3.289     3\n",
      "time_intense[15]     3.723 3.272     3\n",
      "time_intense[16]     1.716 3.287     3\n",
      "time_intense[17]     4.118 3.260     3\n",
      "time_intense[18]     3.629 3.299     3\n",
      "time_intense[19]     2.953 3.281     3\n",
      "time_intense[20]     4.742 3.299     3\n",
      "deviance         42205.201 1.002   820\n",
      "\n",
      "For each parameter, n.eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor (at convergence, Rhat=1).\n",
      "\n",
      "DIC info (using the rule, pD = var(deviance)/2)\n",
      "pD = 565.8 and DIC = 42704.1\n",
      "DIC is an estimate of expected predictive error (lower deviance is better).\n"
     ]
    }
   ],
   "source": [
    "print(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(sim)"
   ]
  }
 ],
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